1. Field of the Invention
The present invention relates to deriving an outcome predictor for a data set in which a number of complex variables affect outcome, and in particular to a method and system of derivation that includes use of a combination of a flexible nonparametric tool and a classification and a recursive partitioning methodology to model complex data.
2. Background of the Technology
There continues to be a need for improved methodologies for addressing difficulties with identifying appropriate outcome predictors for problems involving multiple complex variables potentially affecting outcome, such as are needed to accurately predict outcomes for drugs that will appropriately treat diseases.
For example, one major problem in treating Human Immunodeficiency Virus (HIV)-infected individuals is the appearance of drug-resistant strains of HIV that do not respond to therapy. Maintaining a lower “viral load” (i.e., decreasing the amount of virus in the body) is clinically beneficial both in the short and long term. There are a number of different therapeutic regimens patients may undergo that actively suppress HIV replication and thus lower viral loads. However, prolonged treatment with the currently available drugs, coupled with the relatively high mutation rates of HIV in the body can result in the appearance of drug-resistant strains of HIV. Drug resistant strains are capable of replicating in the presence of therapy, rendering therapy ineffective. This leads to higher viral loads, which in turn produce an adverse clinical prognosis.
Thus, in this example, one problem for the clinician managing the care of the HIV infected individual is developing the optimal therapeutic strategy for maintaining lower viral loads in the presence of ongoing viral mutation. In this regard, the therapeutic regimen may be changed following the emergence of drug-resistant HIV to a more efficacious regimen to which there is not pre-existing resistance that lowers viral loads.
There are various known mutations in the HIV genome that are associated with drug resistance, yet there remains no reliable quantifiable method in the prior art to predict how these mutations will affect the degree to which the virus evades drug therapy. The information derived from viral genotype testing is extremely complex. It is generally not possible to determine an optimal treatment strategy from this data because, for example, the degree of drug resistance and cross-resistance to other drugs is difficult to infer. Cross-resistance in this example is defined as pre-existing resistance of a virus to a drug that has not been taken due to a mutation induced by another drug that has already been taken. This phenomenon primarily occurs because many drugs are very similar to each other and target the same sites in the viral proteins.
There remains an unmet need to provide methods and systems for accurately predicting outcomes to problems having multiple complex variables. For example, there remains an unmet need to provide methods and systems for predicting treatment outcomes, such as drug response, for diseases involving numerous complex variables.